<ul><li>Large language models (LLMs) struggle to recognize low-resource languages, including African languages.</li><li>Combining curated data from African languages with high-quality English educational texts significantly improves the model's performance on these languages.</li><li>On the IrokoBench dataset, the models consistently achieve the best performance compared to other baselines, particularly on knowledge-intensive multiple-choice questions (AfriMMLU).</li><li>The models outperform the base model by over 10% on the cross-lingual question answering benchmark AfriQA.</li></ul>

Lugha-Llama: Adapting Large Language Models for African Languages

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